Analysing and interpreting your data

Guidance on analysing and interpreting your data.

Once you have all of your data, it’s time to analysis it. Your method of data analysis will depend upon the type of data you have collected.

Qualitative data analysis 

Qualitative data analysis describes and summarises the mass of words generated by interviews or observational data.

You do this by breaking your data into manageable units. Synthesise the data and search for patterns to determine what is important.

You’ll use inductive techniques to search for patterns in your data and using them to form a new understanding of your research topic.

Your data analysis may help you expand upon the quantitative data of previous studies or produce a theory which could be developed and tested using advanced analytical techniques.

Although methods of qualitative analysis vary greatly. For example, discourse analysis or grounded theory, the following steps are common.

1. Familiarisation with the data through repeated reading or listening.
2. Transcription of audiovisual material.
3. Organisation and indexing of data for easy retrieval and identification, either by hand or using a dedicated piece of software. For example, NVivo.
4. Anonymising of sensitive data.
5. Coding data.
6. Identification of themes.
7. Development of provisional categories.
8. Exploration of relationships between categories.
9. Refinement of themes and categories.
10. Development of theory and incorporation of pre-existing knowledge.

Quantitative data analysis

Quantitative research techniques generate a mass of numbers that need to be summarised, described and analysed.

Your quantitative data analysis will produce numerical representations such as graphs and charts to describe and explain the phenomena you’ve been researching.

You use deductive techniques in quantitative data analysis. This means that you’ll use a framework to explore your data, from which you’ll either accept or reject your project hypothesis.

Although methods of quantitative data analysis vary greatly, however, the following steps are common.

1. Organisation and indexing of data for easy retrieval and identification, either by hand or using a dedicated piece of software. For example, Statistical Package for the Social Sciences (SPSS) or the open-source software JASP.
2. Reviewing data for completeness.
3. Conducting data entry.
4. Coding data.
5. Undertaking statistical analysis.

Mixed methods data analysis

Mixed method data analysis uses the most appropriate analysis technique for each data set you’ve collected.

Your discussion will combine your interpretation of all data sets into a single coherent narrative.

Suggested reading

• Bruce, N, Pope, D, and Stanistreet, D (2018) Quantitative methods for health research: a practical interactive guide to epidemiology and statistics (2nd edition) Oxford: Wiley is a practical introduction to quantitative research, data collection and data analysis
• Cresswell, J W, and Clark, V L P (2006) Collecting data in mixed methods research in J W Cresswell and V L P Clark (Editors), Designing and conducting mixed methods research (pages 110 to 127) London: Sage - chapter on collecting data in mixed methods research projects
• Denzin, N K, and Lincoln, Y S (2017) The SAGE handbook of qualitative research (5th edition. London: Sage Handbook - for all your queries regarding qualitative research
• Gorman, G E, and Clayton, P (2004) Qualitative research for the information professional: a practical handbook (2nd edition) London: Facet is a comprehensive introduction to all aspects of qualitative research
• IBEC Outcomes Toolkit: STEP 3. Analyzing Data focuses on qualitative data analysis
• Pickard, A J (2013) Research methods in information (2nd ed.). London: Facet Publishing - a practical exploration of the whole research process
• Vaughan, L (2001) Statistical methods for the information professional: a practical, painless approach to understanding, using and interpreting statistics Maryland: American Society for Information Science and Technology is a guide to analysing your quantitative data